Machine Learning Engineer (AI Systems)

Role & Responsibilities

• Design and implement classic ML models (XGBoost, SVMs, clustering, etc.) for structured data use cases.
• Build and optimize deep learning pipelines using PyTorch/TensorFlow, with a focus on NLP and multimodal tasks.
• Develop modern AI systems, including RAG-based applications leveraging LangChain, LlamaIndex, and vector databases (FAISS, Weaviate).
• Architect and experiment with agentic AI systems featuring tool usage, memory, planning, and self-reflection (CrewAI, LangGraph, AutoGen).
• Deploy ML models in production environments using Docker, MLflow, Ray, or Kubernetes.

Preferred Candidate Profile

• 35 years of experience in software or ML engineering.
• Strong proficiency in Python, with hands-on experience in ML model building.
• Exposure to open-source LLMs (e.g., LLaMA, Mistral, Falcon).
• Bonus: Experience in fine-tuning LLMs or building autonomous AI agents.

Key Attributes

• Strong problem-solving and analytical skills.
• Ability to work collaboratively in fast-paced, research-driven environments.
• Passion for exploring cutting-edge AI technologies and applying them to real-world problems.

#MachineLearningEngineer #AIJobs #DeepLearning #NLP #GenerativeAI #LangChain #LLM #DataScienceCareers #AIEngineering #TechHiring